1 Objetivo

O objetivo desse notebook é efetuar todo o processo de modelagem da base de dados adult, disponibilizada para o desafio do curso de introdução ao Machine Learning da Curso-R, utilizando o framework tidymodels. Ou seja, explorar, tratar, preparar, tunnar e escolher o modelo que melhor se ajusta aos dados disponibilizados.

2 Leitura da base

2.1 Informações preliminares

adult <- read_rds("adult.rds")

# head(adult) 

# glimpse(adult)
skim(adult)
-- Data Summary ------------------------
                           Values
Name                       adult 
Number of rows             32561 
Number of columns          16    
_______________________          
Column type frequency:           
  character                9     
  numeric                  7     
________________________         
Group variables            None  

-- Variable type: character ----------------------------------------------------------------------------------------
# A tibble: 9 x 8
  skim_variable  n_missing complete_rate   min   max empty n_unique whitespace
* <chr>              <int>         <dbl> <int> <int> <int>    <int>      <int>
1 workclass           1836         0.944     7    16     0        8          0
2 education              0         1         3    12     0       16          0
3 marital_status         0         1         7    21     0        7          0
4 occupation          1843         0.943     5    17     0       14          0
5 relationship           0         1         4    14     0        6          0
6 race                   0         1         5    18     0        5          0
7 sex                    0         1         4     6     0        2          0
8 native_country       583         0.982     4    26     0       41          0
9 resposta               0         1         4     5     0        2          0

-- Variable type: numeric ------------------------------------------------------------------------------------------
# A tibble: 7 x 11
  skim_variable  n_missing complete_rate     mean        sd    p0    p25    p50    p75    p100 hist 
* <chr>              <int>         <dbl>    <dbl>     <dbl> <dbl>  <dbl>  <dbl>  <dbl>   <dbl> <chr>
1 age                    0             1     38.6     13.6     17     28     37     48      90 ▇▇▅▂▁
2 fnlwgt                 0             1 189778.  105550.   12285 117827 178356 237051 1484705 ▇▁▁▁▁
3 education_num          0             1     10.1      2.57     1      9     10     12      16 ▁▁▇▃▁
4 capital_gain           0             1   1078.    7385.       0      0      0      0   99999 ▇▁▁▁▁
5 capital_loss           0             1     87.3    403.       0      0      0      0    4356 ▇▁▁▁▁
6 hours_per_week         0             1     40.4     12.3      1     40     40     45      99 ▁▇▃▁▁
7 id                     0             1  16281     9400.       1   8141  16281  24421   32561 ▇▇▇▇▇


As variáveis parecem estar com formatos corretos. Ponto de atenção para as variáveis wokclass, occupation e native_country, que apresentam valores missing.

3 AED


Agora vamos analisar o comportamento das variáveis para definirmos como tratar os nossos dados para o modelo.

3.1 Parte 1



# DataExplorer::create_report(adult)

devtools::source_url("https://raw.githubusercontent.com/ricardomattos05/functions/master/function_AED_bivariada.R")
# 
# 
adult2 <- adult %>%
            select(-id) %>% 
            mutate(resposta = if_else(resposta == ">50K", 1, 0))
# 
# 

# names(adult2)
for (i in 1:(length(adult2)-1) ) {
  
  df <- adult2[,c(i,15)]
  cat("### ",names(df[,1]),"\n") 
  print(AED_biv(df,glue("resposta"),"Pre"))
  cat('\n\n')
}

3.1.1 age

NULL

3.1.2 workclass

NULL

3.1.3 fnlwgt

NULL

3.1.4 education

NULL

3.1.5 education_num

NULL

3.1.6 marital_status

NULL

3.1.7 occupation

NULL

3.1.8 relationship

NULL

3.1.9 race

NULL

3.1.10 sex

NULL

3.1.11 capital_gain

NULL

3.1.12 capital_loss

NULL

3.1.13 hours_per_week

NULL

3.1.14 native_country

NULL

Observações:

  • education : é possível visualizar que quanto maior o grau de escolaridade, maior a proporção de pessoas com salarios acima de 50k. E que as categorias abaixo de HS-grad, 1th-4th até 12thalém de serem pouco representativas, possuem baixa proporção, vamos então criar uma categoria uma nova consolidando elas HS-not-grad.

  • marital_status : aqui iremos agrupar os campos Married-AF-spouse e Married-civ-spouse, criando a categoria Married, baseado na similaridade entre elas com relação a variável resposta e considerando a descrição delas.

  • native_country : É um campo com pouca variabilidade, onde 90% dos dados estão atribuídos como “Estados Unidos”. Sendo assim, poderia considerar apenas Estados Unidos e agrupar o restante como outros, mas vamos manter o máximo de informação e reduzir as categorias para 3, agrupando todos os países que obtiveram proporção maior que a média, manter o valor mais representativo e uma categoria com os países abaixo da média.

  • relationship : campo contém os campos husband e wife, aparentemente poderiamos agrupa-los, vamos analisar mais afundo.

  • capital_loss e capital_gain : Aparentemente tanto quem ganha quanto quem perde algum valor apresentam maiores probabilidades de ter salario >50k. Vamos então avaliar a correlação entre elas.

  • workclass : Categorias com baixa representatividade como Never-workede Without-pay não possuem classificação com a resposta de interesse “>50k”, vamos dar um zoom nessa variável e analisar os NA’s que identificamos também.

3.2 Parte 2

3.2.1 occupation


ggplot(adult, aes(x = occupation, fill = resposta)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("occupation")


É possível ver que não faria sentido atribuir os NAs de forma modal, uma vez que nosso objetivo é obter o maior poder preditivo possível, logo, não queremos perder informação. Sendo assim, não vamos diluir os NAs na categoria com maior representatividade Prof-specialty, vamos atribuir à uma categoria com proporções similares e que possui uma boa representatividade, Farming-fishing.

3.2.2 relationship


ggplot(adult, aes(x = relationship)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("relationship")


ggplot(adult, aes(x = relationship, fill = resposta)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("relationship")


ggplot(adult, aes(x = relationship, fill = sex)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("relationship")

Vamos então balancear o gênero agrupando as categorias Wife e Husband, criando a categoria Married.

3.2.3 Capital Gain and Loss


ggplot(adult, aes(x= capital_gain, y= capital_loss)) +
  geom_point()

sum(adult$capital_loss > 0 & adult$capital_gain > 0)
[1] 0

Sendo assim, podemos soma-las e criar a variável capital_total sem medo de perder informação.

3.2.4 Worclass


ggplot(adult, aes(x = workclass)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("Workclass")


ggplot(adult, aes(x = workclass, fill = resposta)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("Workclass")

Pelo visto a catgoria NA possui relação com a variável resposta distinta de todas as outras categorias, vamos então gerar uma nova categoria not-identify para atribuir os valores NA.

3.2.5 native_country


med <- (adult %>% 
          select(resposta) %>%
          filter(resposta == ">50K") %>% 
          count() %>% 
          as.numeric())/nrow(adult)
         

tb_country<- adult %>% 
                select(native_country, resposta) %>% 
                group_by(native_country) %>% 
                count(resposta) %>% 
                mutate(prop = prop.table(n)) %>% 
                filter(resposta == ">50K") %>% 
                mutate( class = case_when( native_country == "United-States" ~ "United-States",
                                           prop > med ~ ">mean",
                                           prop <= med ~ "<=mean" )  )

tb_country %>% 
  select(native_country,class) %>% 
  group_by(class) %>% 
  count()
NA
NA
NA

Ficamos então com 21 países com proporções abaixo da méda, 18 acima e “United-States” como as 3 categorias restantes.


A distribuição ficou com 5% para países acima da média e 5% para países abaixo da média.

4 Modelagem

Com nossa a análise exploratória concluída, vamos dar início as estapas da modelagem utilizando o framework do tidymodels.

4.1 Amostragem

Fazendo a separação dos dados em treino e teste, estratificando pela variável resposta para a modelagem.

set.seed(32)

adult_split <- initial_split(adult, prop = 0.8, strata = resposta)

adult_train <- training(adult_split)
adult_test <- testing(adult_split)

4.2 Data Prep

Os tratamentos necessários observados na AED, que foi feita utilizando o pacote DataExplorer e a função AED_biv que gerei para entender o comportamento das variáveis com relação a variável resposta, serão armazenados utilizando o recipes para ser utilizado tanto para treinar os modelos como para testar posteriormente.

4.3 Cross-Validation

Especificando a validação cruzada:

set.seed(32)
adult_vfold <- vfold_cv(adult_train, v = 5, strata = resposta)
adult_vfold
#  5-fold cross-validation using stratification 

4.4 Modelos

Os modelos que serão ajustados:

  • Decision tree
  • Random Forest
  • Xgboost

Obs: Os valores dos hiperparâmetros foram obtidos a partir da tunagem e inseridos apenas para otimizar o tempo de renderização do script.

4.4.1 Decision tree

Especificando modelo:

 #1.069415e-09  8   19  Model04
adult_tree
Decision Tree Model Specification (classification)

Main Arguments:
  cost_complexity = 1.069415e-09
  tree_depth = 8
  min_n = 19

Computational engine: rpart 

Workflow para decision tree:

workflow_adult_tree
== Workflow ====================================================================
Preprocessor: Recipe
Model: decision_tree()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Decision Tree Model Specification (classification)

Main Arguments:
  cost_complexity = 1.069415e-09
  tree_depth = 8
  min_n = 19

Computational engine: rpart 

Parâmentros:

hiperparams <- parameters(
 adult_tree
)
hiperparams
Collection of 3 parameters for tuning

              id  parameter type object class
 cost_complexity cost_complexity    nparam[+]
      tree_depth      tree_depth    nparam[+]
           min_n           min_n    nparam[+]

Grid:

Efetuando tunagem de hiperparâmetros:

Finalizando WF:

workflow_tree_final
== Workflow ====================================================================
Preprocessor: Recipe
Model: decision_tree()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Decision Tree Model Specification (classification)

Main Arguments:
  cost_complexity = 1.069415e-09
  tree_depth = 8
  min_n = 19

Computational engine: rpart 

Verificando importância dos atributos:

Modelo final:

4.4.2 Random Forest

Especificando modelo:

# 23  1715    21
adult_rf
Random Forest Model Specification (classification)

Main Arguments:
  mtry = 23
  trees = 1715
  min_n = 21

Computational engine: randomForest 

Workflow para random forest:


workflow_adult_rf <- 
  adult_wf %>% 
  add_model(adult_rf)

Grid:

parameters(adult_rf)
Collection of 0 parameters for tuning

[1] id             parameter type object class  
<0 linhas> (ou row.names de comprimento 0)

Efetuando tunagem de hiperparâmetros:

set.seed(123)
rf_tune<- 
  workflow_adult_rf %>% 
  tune_grid(
    resamples = adult_vfold,
    grid = rf_grid,
    control = control_grid(save_pred = TRUE, verbose = T, allow_par = T),
    metrics = metric_set(roc_auc)
  )
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rf_best_hiperparams <- select_best(rf_tune) 
Error in .get_tune_metric_names(x) : objeto 'rf_tune' não encontrado

Finalizando WF:

workflow_rf_final
== Workflow ====================================================================
Preprocessor: Recipe
Model: rand_forest()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Random Forest Model Specification (classification)

Main Arguments:
  mtry = 23
  trees = 1715
  min_n = 21

Computational engine: randomForest 

Verificando importância dos atributos:

Modelo final:

4.4.3 Xgboost

Como o Xgboost possui muitos parâmetros, optei pora não tunnar os parâmetros loss_reduction e samples_size nesse primeiro momento. Sendo assim os valores default da enginee xgboost são atribuídos à esses parâmetros, loss_reduction = 0 e sample_size = 1.

adult_xgb
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106

Computational engine: xgboost 

Workflow para Xgboost:

workflow_adult_xgb
== Workflow ====================================================================
Preprocessor: Recipe
Model: boost_tree()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106

Computational engine: xgboost 

Grid:

xgb_grid <- parameters(adult_xgb) %>%
    finalize(bake(prep(adult_recipe),adult_train)) %>%
    grid_max_entropy(size = 20)
Erro: At least one parameter object is required.


Efetuando tunagem de hiperparâmetros:

library(doFuture)
all_cores <- parallel::detectCores(logical = FALSE) - 1

registerDoFuture()
cl <- makeCluster(all_cores)
plan(future::cluster, workers = cl)
getDoParWorkers()
[1] 3
# grid search
ini <- Sys.time()
xgb_tune <-
  workflow_adult_xgb %>%
    tune_grid(
        resamples = adult_vfold,
        grid = xgb_grid,
        control = control_grid(verbose = TRUE),
        metrics = metric_set(roc_auc)
    )
Warning in x :
  encerrando conexão não utilizada 6 (<-WNB027899SPO.ciandt.global:11937)
Warning in x :
  encerrando conexão não utilizada 5 (<-WNB027899SPO.ciandt.global:11937)
Warning in x :
  encerrando conexão não utilizada 4 (<-WNB027899SPO.ciandt.global:11937)
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Sys.time()- ini #Time difference of 39.9844 mins(parallel)
Time difference of 48.61914 mins
foreach::registerDoSEQ()
xgb_best_hiperparams 
Erro: objeto 'xgb_best_hiperparams' não encontrado

Finalizando WF:

workflow_xgb_final
== Workflow ====================================================================
Preprocessor: Recipe
Model: boost_tree()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106

Computational engine: xgboost 

Verificando importância dos atributos:

workflow_xgb_final %>%
  fit(adult_train) %>%
  pull_workflow_fit() %>%
  vip::vip(geom = "col")
`as.tibble()` is deprecated as of tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

Modelo final:

4.4.4 Xgboost2

Agora vamos inserir os valores identificados na tunagem para os parâmetros e efetuar o tuning para os parâmetros que sample_size e loss_reduction:

adult_xgb2
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106445
  loss_reduction = 0.000127
  sample_size = 0.989

Computational engine: xgboost 

Workflow para Xgboost:

workflow_adult_xgb2
== Workflow ====================================================================
Preprocessor: Recipe
Model: boost_tree()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106445
  loss_reduction = tune()
  sample_size = tune()

Computational engine: xgboost 

Grid:

Efetuando tunagem de hiperparâmetros:

getDoParWorkers()
[1] 3

Finalizando WF:

workflow_xgb_final2
== Workflow ====================================================================
Preprocessor: Recipe
Model: boost_tree()

-- Preprocessor ----------------------------------------------------------------
7 Recipe Steps

* step_mutate()
* step_rm()
* step_string2factor()
* step_normalize()
* step_zv()
* step_novel()
* step_dummy()

-- Model -----------------------------------------------------------------------
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106445
  loss_reduction = 0.000127
  sample_size = 0.989

Computational engine: xgboost 

Verificando importância dos atributos:

Modelo final:

5 Comparando os modelos

Podemos ver a partir da curva roc que o xgboost obteve melhor perfomance que o random forest e a árvore de decisão. Interessante que o xgboost sem tunar os hiperparâmetros loss_reduction e sample_size se saiu discretamente melhor que o xgboost2 onde efetuamos o tuning desses dois hiperparâmetros. Sendo assim nosso modelo final será o xgb_final.

6 Scoragem para submeter resultado

Vamos então finalizar nosso modelo campeão e scorar a base de validação para efetuar a submissão:

xgboost_modelo_final
Boosted Tree Model Specification (classification)

Main Arguments:
  mtry = 34
  trees = 1309
  min_n = 5
  tree_depth = 10
  learn_rate = 0.0106

Computational engine: xgboost 

Matriz de confusão:

adult_val %>% 
  transmute(resposta = factor(resposta, levels = c(">50K", "<=50K")), 
            more_than_50k = ifelse(more_than_50k > 0.5, ">50K", "<=50K") %>% 
              factor(levels = c(">50K", "<=50K"))) %>% 
  table() %>% 
  caret::confusionMatrix()
Confusion Matrix and Statistics

        more_than_50k
resposta  >50K <=50K
   >50K   2505  1341
   <=50K   719 11716
                                          
               Accuracy : 0.8735          
                 95% CI : (0.8683, 0.8785)
    No Information Rate : 0.802           
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.6286          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.7770          
            Specificity : 0.8973          
         Pos Pred Value : 0.6513          
         Neg Pred Value : 0.9422          
             Prevalence : 0.1980          
         Detection Rate : 0.1539          
   Detection Prevalence : 0.2362          
      Balanced Accuracy : 0.8371          
                                          
       'Positive' Class : >50K            
                                          

Selecionando campos no formato da submissão:

submissao <- adult_val %>% select(id, more_than_50k)
write_csv(submissao, "submissao.csv")
---
title: "Desafio Intro ML - Curso-R"
author: "Ricardo Mattos"
date: "12/07/2020"
output:
  html_document:
    toc: yes
    toc_float: yes
    number_sections: yes
  html_notebook:
    toc: yes
    toc_float: yes
    number_sections: yes
---


```{r setup, include=FALSE}
library(readr)
library(tidymodels)
library(ggplot2)
library(skimr)
library(RCurl)
library(kableExtra)
library(gridExtra)
library(glue)
library(forcats)
library(DataExplorer)
library(e1071)
```

# Objetivo

O objetivo desse notebook é efetuar todo o processo de modelagem da base de dados `adult`, disponibilizada para o desafio do curso de introdução ao Machine Learning da Curso-R, utilizando o framework `tidymodels`. Ou seja, explorar, tratar, preparar, tunnar e escolher o modelo que melhor se ajusta aos dados disponibilizados.


# Leitura da base

## Informações preliminares

```{r}
adult <- read_rds("adult.rds")

# head(adult) 

# glimpse(adult)
skim(adult)

```



<br> As variáveis parecem estar com formatos corretos. Ponto de atenção para as variáveis `wokclass`, `occupation` e `native_country`, que apresentam valores missing.  </br>


# AED 

<br> Agora vamos analisar o comportamento das variáveis para definirmos como tratar os nossos dados para o modelo. </br>

## Parte 1 {.tabset}

```{r,results='asis', echo=TRUE, message=FALSE, warning=FALSE}


# DataExplorer::create_report(adult)

devtools::source_url("https://raw.githubusercontent.com/ricardomattos05/functions/master/function_AED_bivariada.R")
# 
# 
adult2 <- adult %>%
            select(-id) %>% 
            mutate(resposta = if_else(resposta == ">50K", 1, 0))
# 
# 

# names(adult2)
for (i in 1:(length(adult2)-1) ) {
  
  df <- adult2[,c(i,15)]
  cat("### ",names(df[,1]),"\n") 
  print(AED_biv(df,glue("resposta"),"Pre"))
  cat('\n\n')
}



```


## {-}

Observações:

* `education` : é possível visualizar que quanto maior o grau de escolaridade, maior a proporção de pessoas com salarios acima de 50k. E que as categorias abaixo de HS-grad, `1th-4th` até `12th`além de serem pouco representativas, possuem baixa proporção, vamos então criar uma categoria uma nova consolidando elas `HS-not-grad`.

* `marital_status` : aqui iremos agrupar os campos `Married-AF-spouse` e `Married-civ-spouse`, criando a categoria `Married`, baseado na similaridade entre elas com relação a variável resposta e considerando a descrição delas.

* `native_country` : É um campo com pouca variabilidade, onde `r (adult %>% select(native_country) %>% filter(native_country == "United-States") %>% count() / count(adult)) %>% as.numeric() %>% percent()` dos dados estão atribuídos como "Estados Unidos". Sendo assim, poderia considerar apenas Estados Unidos e agrupar o restante como outros, mas vamos manter o máximo de informação e reduzir as categorias para 3, agrupando todos os países que obtiveram proporção maior que a média, manter o valor mais representativo e uma categoria com os países abaixo da média.

* `relationship` : campo contém os campos `husband` e `wife`, aparentemente poderiamos agrupa-los, vamos analisar mais afundo.

* `capital_loss` e `capital_gain` : Aparentemente tanto quem ganha quanto quem perde algum valor apresentam maiores probabilidades de ter salario >50k. Vamos então avaliar a correlação entre elas.

* `workclass` : Categorias com baixa representatividade como `Never-worked`e `Without-pay` não possuem classificação com a resposta de interesse ">50k", vamos dar um zoom nessa variável e analisar os NA's que identificamos também.

## Parte 2 {.tabset}

### occupation

```{r}

ggplot(adult, aes(x = occupation, fill = resposta)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("occupation")

```

<br> É possível ver que não faria sentido atribuir os NAs de forma modal, uma vez que nosso objetivo é obter o maior poder preditivo possível, logo, não queremos perder informação. Sendo assim, não vamos diluir os NAs na categoria com maior representatividade `Prof-specialty`, vamos atribuir à uma categoria com proporções similares e que possui uma boa representatividade, `Farming-fishing`. </br>


### relationship
```{r}

ggplot(adult, aes(x = relationship)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("relationship")

ggplot(adult, aes(x = relationship, fill = resposta)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("relationship")

ggplot(adult, aes(x = relationship, fill = sex)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("relationship")

```

Vamos então balancear o gênero agrupando as categorias Wife e Husband, criando a categoria `Married`.

### Capital Gain and Loss


```{r, echo = TRUE}

ggplot(adult, aes(x= capital_gain, y= capital_loss)) +
  geom_point()
```



```{r}
sum(adult$capital_loss > 0 & adult$capital_gain > 0)
```
Sendo assim, podemos soma-las e criar a variável `capital_total` sem medo de perder informação.

### Worclass

```{r}

ggplot(adult, aes(x = workclass)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("Workclass")

ggplot(adult, aes(x = workclass, fill = resposta)) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("Workclass")

```

Pelo visto a catgoria NA possui relação com a variável resposta distinta de todas as outras categorias, vamos então gerar uma nova categoria `not-identify` para atribuir os valores NA.


### native_country

```{r}

med <- (adult %>% 
          select(resposta) %>%
          filter(resposta == ">50K") %>% 
          count() %>% 
          as.numeric())/nrow(adult)
         

tb_country<- adult %>% 
                select(native_country, resposta) %>% 
                group_by(native_country) %>% 
                count(resposta) %>% 
                mutate(prop = prop.table(n)) %>% 
                filter(resposta == ">50K") %>% 
                mutate( class = case_when( native_country == "United-States" ~ "United-States",
                                           prop > med ~ ">mean",
                                           prop <= med ~ "<=mean" )  )

tb_country %>% 
  select(native_country,class) %>% 
  group_by(class) %>% 
  count()



```


Ficamos então com 21 países com proporções abaixo da méda, 18 acima e "United-States" como as 3 categorias restantes.


```{r, echo=FALSE}

# tb_country %>%
#         filter(class == "<=mean") %>%
#         select(native_country) %>%
#         as.factor()


adult2<- adult2 %>%
    mutate(class_country = case_when(native_country %in% c("Cambodia", "Canada", "China", "Cuba", "England", "France", "Germany", "Greece", "Hong", "India", "Iran", "Italy", "Japan", "Philippines", "Scotland", "Taiwan", "Yugoslavia", NA)  ~ ">mean",
                             native_country == "United-States" ~ "United-States",
                             TRUE ~ "<=mean") )     

# adult2 %>% 
#   filter(class == ">mean") %>% 
#   select(native_country) %>% 
#   group_by(native_country) %>% 
#   count()

ggplot(adult2, aes(x = class_country)) +
  geom_bar(aes(y = (..count..)/sum(..count..))) +
  geom_text(stat = "count", 
            aes(label = round((..count..)/sum(..count..), 2), y = ..prop.. + 0.02))+
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_y_continuous(labels=percent)+ ylab("prop")+
  ggtitle("class_country")

ggplot(adult2, aes(x = class_country, fill = as.factor(resposta) )) + 
  geom_bar(position="fill") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_y_continuous(labels=percent)+
  ggtitle("class_country")
    
    
```

<br>A distribuição ficou com 5% para países acima da média e 5% para países abaixo da média.</br>

# Modelagem

Com nossa a análise exploratória concluída, vamos dar início as estapas da modelagem utilizando o framework do `tidymodels`.

## Amostragem

Fazendo a separação dos dados em treino e teste, estratificando pela variável resposta para a modelagem.

```{r, echo=TRUE, message=FALSE, warning=FALSE}
set.seed(32)

adult_split <- initial_split(adult, prop = 0.8, strata = resposta)

adult_train <- training(adult_split)
adult_test <- testing(adult_split)

```


## Data Prep

Os tratamentos necessários observados na AED, que foi feita utilizando o pacote `DataExplorer` e a função [`AED_biv`](https://github.com/ricardomattos05/functions/blob/master/function_AED_bivariada.R) que gerei para entender o comportamento das variáveis com relação a variável resposta, serão armazenados utilizando o recipes para ser utilizado tanto para treinar os modelos como para testar posteriormente.


```{r}

adult_recipe <- 
  recipe(resposta ~ ., data = adult_train) %>% 
  step_mutate(
    
    occupation = case_when(
      is.na(occupation) ~ "Farming-fishing",
      TRUE ~ as.character(occupation)),
    
    workclass = case_when(
      is.na(workclass) ~ "Not-identify",
      TRUE ~ as.character(workclass)),
    
    class_country = case_when(native_country %in% c("Cambodia", "Canada", "China", "Cuba", "England", "France", "Germany", "Greece", "Hong", "India", "Iran", "Italy", "Japan", "Philippines", "Scotland", "Taiwan", "Yugoslavia", NA) ~ "greater_mean",
                             native_country == "United-States" ~ "United-States",
                             TRUE ~ "smaller_mean")
    ,
    
    capital_total = capital_gain + capital_loss
    , 
    
    marital_status = case_when(
      marital_status %in% c("Married-AF-spouse" , "Married-civ-spouse") ~ "Married",
      TRUE ~ as.character(marital_status))
    ,
    
    education = case_when(education %in% c("1st-4th", "5th-6th", "7th-8th", "9th", "10th", "11th", "12th") ~ "HS-not-grad",
                          TRUE ~ as.character(education))
    ,
    
    relationship = case_when(  relationship %in% c("Husband","Wife") ~ "Married",
                               TRUE ~ as.character(relationship))
    
  ) %>% 
  step_rm(id, capital_gain, capital_loss, native_country)%>% 
  step_string2factor(all_nominal()) %>%
  step_normalize(all_numeric()) %>% 
  step_zv(all_predictors()) %>%
  step_novel(all_nominal(), -all_outcomes()) %>% 
  step_dummy(all_nominal(), -all_outcomes())

 # bake(prep(adult_recipe), adult_train)


adult_wf <- 
  workflow() %>% 
  add_recipe(adult_recipe)
```



## Cross-Validation

Especificando a validação cruzada:

```{r}
set.seed(32)
adult_vfold <- vfold_cv(adult_train, v = 5, strata = resposta)
adult_vfold
```

## Modelos {.tabset}

Os modelos que serão ajustados:

  * Decision tree
  * Random Forest
  * Xgboost
  
Obs: Os valores dos hiperparâmetros foram obtidos a partir da tunagem e inseridos apenas para otimizar o tempo de renderização do script.
  
### Decision tree

Especificando modelo:

```{r}
adult_tree <- 
  decision_tree(
    min_n = 19,
    cost_complexity = 1.069415e-09, 
    tree_depth = 8) %>%
  set_mode("classification") %>%
  set_engine("rpart")
 #1.069415e-09	8	19	Model04
adult_tree
```
Workflow para decision tree:

```{r}

workflow_adult_tree <- 
  adult_wf %>% 
  add_model(adult_tree)


```

Parâmentros:

```{r}
# hiperparams <- parameters(
#  adult_tree
# )
# hiperparams
```

Grid:

```{r}
# set.seed(32)
# tree_grid <- grid_max_entropy(hiperparams, size = 10)
# tree_grid

```


Efetuando tunagem de hiperparâmetros:

```{r, echo= TRUE, results="hide",include=FALSE}

# tree_tune <- 
#   workflow_adult_tree %>% 
#   tune_grid(
#     resamples = adult_vfold,
#     grid = tree_grid,
#     control = control_grid(save_pred = TRUE, verbose = FALSE, allow_par = F),
#     metrics = metric_set(roc_auc)
#   )

```


```{r}
# autoplot(tree_tune)
# show_best(tree_tune, "roc_auc")
# 
# tree_best_hiperparams <- select_best(tree_tune) #1.069415e-09	8	19	Model04 (roc_auc = 0.8993608)
# tree_best_hiperparams


```

Finalizando WF:

```{r}
workflow_tree_final <- finalize_workflow(
  workflow_adult_tree,
  #tree_best_hiperparams
  parameters(workflow_adult_tree)
)

workflow_tree_final
```


Verificando importância dos atributos:

```{r}
workflow_tree_final %>%
  fit(adult_train) %>%
  pull_workflow_fit() %>%
  vip::vip(geom = "col")
```


Modelo final:

```{r}

tree_final <- last_fit(workflow_tree_final, adult_split)
collect_metrics(tree_final)

```



### Random Forest

Especificando modelo:

```{r}
adult_rf <- 
  rand_forest(
    min_n = 21,
    mtry = 23,
    trees = 1715) %>%
  set_mode("classification") %>%
  set_engine("randomForest")
# 23  1715    21
adult_rf
```

Workflow para random forest:

```{r}

workflow_adult_rf <- 
  adult_wf %>% 
  add_model(adult_rf)


```


Grid:

```{r}
# set.seed(32)
# 
# rf_grid <- parameters(adult_rf) %>% 
#   finalize(bake(prep(adult_recipe), adult_train)) %>% 
#   grid_max_entropy(size = 10)
# 
# rf_grid
```

Efetuando tunagem de hiperparâmetros:

```{r}

# library(doParallel)
# library("doFuture")
# 
# all_cores <- parallel::detectCores(logical = FALSE) - 1
# registerDoFuture()
# cl <- makeCluster(all_cores)
# plan(future::cluster, workers = cl)
# getDoParWorkers()
# 
# set.seed(123)
# rf_tune<- 
#   workflow_adult_rf %>% 
#   tune_grid(
#     resamples = adult_vfold,
#     grid = rf_grid,
#     control = control_grid(save_pred = TRUE, verbose = FALSE, allow_par = T),
#     metrics = metric_set(roc_auc)
#   )

```

```{r}
# autoplot(rf_tune)
# show_best(rf_tune,"roc_auc")
# 
# rf_best_hiperparams <- select_best(rf_tune) 
# rf_best_hiperparams 

##    mtry trees min_n .config
##   <int> <int> <int> <chr>  
## 1    23  1715    21 Model01
```

Finalizando WF:

```{r}
workflow_rf_final <- finalize_workflow(
  workflow_adult_rf,
  #rf_best_hiperparams
  parameters(workflow_adult_rf)
)

workflow_rf_final
```

Verificando importância dos atributos:

```{r}
workflow_rf_final %>%
  fit(adult_train) %>%
  pull_workflow_fit() %>%
  vip::vip(geom = "col")

```


Modelo final:

```{r}

rf_final <- last_fit(workflow_rf_final, adult_split)
collect_metrics(rf_final) #roc_auc = 0.9094242

```

### Xgboost

Como o Xgboost possui muitos parâmetros, optei pora não tunnar os parâmetros `loss_reduction` e `samples_size` nesse primeiro momento. Sendo assim os valores default da enginee `xgboost` são atribuídos à esses parâmetros, loss_reduction = 0 e sample_size = 1.

```{r}
adult_xgb <- 
  boost_tree(
   mtry = 34, 
  trees = 1309, 
  min_n = 5, 
  tree_depth = 10,
  # loss_reduction = tune(), 
  learn_rate = 0.0106, 
  # sample_size = tune()
  ) %>%
  set_mode("classification") %>%
  set_engine("xgboost")

##    mtry trees min_n tree_depth learn_rate .config
##   <int> <int> <int>      <int>      <dbl> <chr>  
## 1    34  1309     5         10     0.0106 Model13

adult_xgb
```

Workflow para Xgboost:

```{r}

workflow_adult_xgb <- 
  adult_wf %>% 
  add_model(adult_xgb)

workflow_adult_xgb
```

Grid:

```{r}
# set.seed(32)
# 
# xgb_grid <- parameters(adult_xgb) %>%
#     finalize(bake(prep(adult_recipe),adult_train)) %>%
#     grid_max_entropy(size = 20)
# 
# xgb_grid


```

<br>Efetuando tunagem de hiperparâmetros:</br>

```{r}
# library(doFuture)
# all_cores <- parallel::detectCores(logical = FALSE) - 1
# 
# registerDoFuture()
# cl <- makeCluster(all_cores)
# plan(future::cluster, workers = cl)
# getDoParWorkers()
# 
# # grid search
# ini <- Sys.time()
# xgb_tune <-
#   workflow_adult_xgb %>%
#     tune_grid(
#         resamples = adult_vfold,
#         grid = xgb_grid,
#         control = control_grid(verbose = FALSE),
#         metrics = metric_set(roc_auc)
#     )
# Sys.time()- ini #Time difference of 39.9844 mins(parallel)
# 
# foreach::registerDoSEQ()
```


```{r}
# autoplot(xgb_tune)
# show_best(xgb_tune,"roc_auc")
# 
# xgb_best_hiperparams <- select_best(xgb_tune)
# xgb_best_hiperparams 

##    mtry trees min_n tree_depth learn_rate .config
##   <int> <int> <int>      <int>      <dbl> <chr>  
## 1    34  1309     5         10     0.0106 Model13

```
Finalizando WF:

```{r}
workflow_xgb_final <- finalize_workflow(
  workflow_adult_xgb,
  #xgb_best_hiperparams
  parameters(workflow_adult_xgb)
)

workflow_xgb_final
```


Verificando importância dos atributos:

```{r, message=FALSE, warning=FALSE}
workflow_xgb_final %>%
  fit(adult_train) %>%
  pull_workflow_fit() %>%
  vip::vip(geom = "col")
```

Modelo final:

```{r}

xgb_final <- last_fit(workflow_xgb_final, adult_split)
collect_metrics(xgb_final) #0.9246310

```

### Xgboost2

Agora vamos inserir os valores identificados na tunagem para os parâmetros e efetuar o tuning para os parâmetros que `sample_size` e `loss_reduction`:

```{r}
adult_xgb2 <- 
  boost_tree(
   mtry = 34, 
  trees = 1309, 
  min_n = 5, 
  tree_depth = 10,
  loss_reduction = 0.000127,#tune(),
  learn_rate = 0.0106445, 
  sample_size = 0.989,#tune()
  ) %>%
  set_mode("classification") %>%
  set_engine("xgboost")

##   loss_reduction sample_size .config
##            <dbl>       <dbl> <chr>  
## 1       0.000127       0.989 Model09

adult_xgb2

``` 

Workflow para Xgboost:

```{r}

workflow_adult_xgb2 <- 
  adult_wf %>% 
  add_model(adult_xgb2)

```

Grid:

```{r}
# set.seed(32)
# 
# xgb_grid2 <- parameters(adult_xgb2) %>%
#     grid_max_entropy(size = 20)
# 
# xgb_grid2


```

Efetuando tunagem de hiperparâmetros:

```{r}

# all_cores <- parallel::detectCores(logical = FALSE) - 1
# 
# registerDoFuture()
# cl <- makeCluster(all_cores)
# plan(future::cluster, workers = cl)
# getDoParWorkers()
# 
# # grid search
# ini <- Sys.time()
# xgb_tune2 <-
#   workflow_adult_xgb2 %>%
#     tune_grid(
#         resamples = adult_vfold,
#         grid = xgb_grid2,
#         control = control_grid(verbose = FALSE),
#         metrics = metric_set(roc_auc)
#     )
# Sys.time()- ini
# 
# foreach::registerDoSEQ()
```


```{r}
# autoplot(xgb_tune2)
# show_best(xgb_tune2,"roc_auc")
# 
# xgb2_best_hiperparams <- select_best(xgb_tune2)
# xgb2_best_hiperparams 

```
Finalizando WF:

```{r}
workflow_xgb_final2 <- finalize_workflow(
  workflow_adult_xgb2,
  # xgb2_best_hiperparams
  parameters(workflow_adult_xgb2)
)

workflow_xgb_final2
```

Verificando importância dos atributos:

```{r}

workflow_xgb_final2 %>%
  fit(adult_train) %>%
  pull_workflow_fit() %>%
  vip::vip(geom = "col")

```

Modelo final:

```{r}

xgb2_final <- last_fit(workflow_xgb_final2, adult_split)
collect_metrics(xgb2_final) #0.9243285

```

# Comparando os modelos

```{r}
bind_rows(
 tree_final %>%
  collect_predictions() %>% 
  mutate(id = "Decision tree")
  ,
 rf_final %>%
  collect_predictions() %>%
  mutate(id = "Random Forest")
 ,
  xgb_final %>%
  collect_predictions() %>%
  mutate(id = "xgboost")
  ,
  xgb2_final %>%
  collect_predictions() %>%
  mutate(id = "xgboost2")
) %>% 
  group_by(id) %>% 
  nest() %>% 
  ungroup() %>% 
  mutate(roc = map(data, ~roc_curve(.x, truth = resposta, `.pred_<=50K`)),
         auc = map_dbl(data, ~roc_auc(.x, truth = resposta, `.pred_<=50K`) %>% 
                         pull(.estimate) %>% round(3)),
         id = paste0(id, " auc: ", auc)) %>% 
  select(-data) %>% 
  unnest(cols = c(roc)) %>% 
  ggplot() +
  aes(x = 1 - specificity, y = sensitivity, color = id) +
  geom_path() +
  geom_abline(lty = 3) +
  ggtitle("Curva Roc")


```

Podemos ver a partir da curva roc que o xgboost obteve melhor perfomance que o random forest e a árvore de decisão. 
Interessante que o xgboost sem tunar os hiperparâmetros `loss_reduction` e `sample_size` se saiu discretamente melhor que o xgboost2 onde efetuamos o tuning desses dois hiperparâmetros. Sendo assim nosso modelo final será o **xgb_final**.

# Scoragem para submeter resultado

Vamos então finalizar nosso modelo campeão e scorar a base de validação para efetuar a submissão:

```{r}

adult_val <- readr::read_rds("adult_val.rds")

xgboost_modelo_final <- adult_xgb #%>% 
    # finalize_model(xgb_best_hiperparams)

adult_fit <- 
  fit(xgboost_modelo_final,
    formula = resposta ~ .,  
    data = bake(prep(adult_recipe), new_data = adult))

adult_val$more_than_50k <- 
  predict(adult_fit, 
          bake(prep(adult_recipe), new_data = adult_val),
          type = "prob")$`.pred_>50K`
```

Matriz de confusão:

```{r}

adult_val %>% 
  transmute(resposta = factor(resposta, levels = c(">50K", "<=50K")), 
            more_than_50k = ifelse(more_than_50k > 0.5, ">50K", "<=50K") %>% 
              factor(levels = c(">50K", "<=50K"))) %>% 
  table() %>% 
  caret::confusionMatrix()


```


Selecionando campos no formato da submissão:

```{r}
submissao <- adult_val %>% select(id, more_than_50k)
write_csv(submissao, "submissao.csv")
```





